The time-varying communication and computing properties of UAV-based networked airborne computing systems can be modeled as uncertain stragglers that are slow in generating the result or take a long time to transmit data. Topology changes or link/node failures can also be modeled as uncertain stragglers that fail to generate or return any results. To alleviate the effects of stragglers, coded distributed computing (CDC) [1]} is a promising technique, which introduces computation redundancy into the system via exploiting the coding theory. Currently, most works on CDC focus on the matrix multiplication problem or assume homogeneous distributed systems with static computing nodes [2]}, [3]}, [4]}, [5]}. However, many data analysis algorithms, especially the filtering or feature extraction techniques like the convolutional neural networks (CNNs), involve convolution operations. How to perform resilient distributed convolution over UAV-based NAC systems formed by heterogeneous moving UAVs has not been investigated, to the best of our knowledge. The state-of-the-art coded convolution strategy introduced in [6]} was designed for homogeneous systems with static computing nodes, which performs poorly over the UAV-based NAC system as we will show in the simulation studies. Although there have been some works considering heterogeneous systems [5]}, [3]}, [4]} and moving computing nodes [10]}, [11]}, these works are centered on the matrix multiplication problem, which has a quite different problem solving procedure from the convolution problem.
